Described are methods, systems and computer readable media for dynamic updating of query result displays.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

A messaging system provides recommendations of content that account holders of the messaging system might be interested in engaging with. In order to determine what to recommend, the messaging system generates a model of account holder engagement behavior organized by type of engagement. The model parameters are trained on differences between expected engagement behavior based on past data and actual engagement behavior, and include a set of common factor matrices that are trained using data from more than on engagement type. As a consequence, engagement behavior of other account holders with respect to other types of engagements different than the one sought to be recommended serves as a partial basis for determining what engagements of the sought-after type are recommended.

Apparatus and methods for sharing bookmarks are provided. Bookmarks may include queries to a chatbot or other interactive application. A server may include a shared bookmark controller and a personal computing device may include a user bookmark controller. A user may save a bookmark and share the bookmark with a chosen recipient. The user bookmark controller may send the bookmark and the identity of the recipient to the shared bookmark controller. The shared bookmark controller may then send the bookmark to the recipient and record whether the recipient saves or rejects the bookmark. Bookmarks may be collated and categorized into groups, and entire groups may be shared.

Apparatus and methods for sharing bookmarks are provided. Bookmarks may include queries to a chatbot or other interactive application. A server may include a shared bookmark controller and a personal computing device may include a user bookmark controller. A user may save a bookmark and share the bookmark with a chosen recipient. The user bookmark controller may send the bookmark and the identity of the recipient to the shared bookmark controller. The shared bookmark controller may then send the bookmark to the recipient and record whether the recipient saves or rejects the bookmark. Bookmarks may be collated and categorized into groups, and entire groups may be shared.

An example method of managing memory in a computer system implementing non-uniform memory access (NUMA) by a plurality of sockets each having a processor component and a memory component is described. The method includes replicating page tables for an application executing on a first socket of the plurality of sockets across each of the plurality of sockets; associating metadata for pages of the memory storing the replicated page tables in each of the plurality of sockets; and updating the replicated page tables using the metadata to locate the pages of the memory that store the replicated page tables.

An example method of managing memory in a computer system implementing non-uniform memory access (NUMA) by a plurality of sockets each having a processor component and a memory component is described. The method includes replicating page tables for an application executing on a first socket of the plurality of sockets across each of the plurality of sockets; associating metadata for pages of the memory storing the replicated page tables in each of the plurality of sockets; and updating the replicated page tables using the metadata to locate the pages of the memory that store the replicated page tables.

Described are methods, systems and computer readable media for performance logging of complex query operations.

A method and system for executing database queries in parallel using a shared metadata store. The metadata store may reside on a master node, where the master node is the root node in a tree. The master node may distribute query plans and query metadata to other nodes in the cluster. These additional nodes may request additional metadata from each other or the master nodes as necessary.

A method and system operable for: receiving a search query including search terms; using a machine learning module, selecting features of the search terms and mapping an association between the search terms and a domain object, thereby generating a domain object classification; tagging the domain object with the domain object classification; and using the domain object tagged with the domain object classification to conduct a subsequent search. Conducting the subsequent search includes: receiving a subsequent search query including subsequent search terms; tokenizing the subsequent search terms; finding permutations of the tokenized subsequent search terms; matching the subsequent search terms to the domain object tagged with the domain object classification; and displaying subsequent search results via a user interface.